Ending unexpected drug toxicity.

Axiom is building the translational intelligence layer for scientists, using AI to connect lab experiments to human drug outcomes and help scientists develop safer medicines.

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Methods

Unexpected toxicity shouldn’t be a leading cause of drug failure.

Predict toxicity earlier with AI that outperforms animal testing.

Understand drug toxicity before it reaches humans.

01 Accurate Human Predictions

Many preclinical candidates and clinical trials fail because of unexpected toxicity. Axiom uses human-relevant experimental data and intelligent reasoning to help scientists identify risk earlier and reduce these failures.

02 The World’s Richest Dataset

We’ve profiled thousands of clinical-stage molecules across a broad suite of proprietary experiments, creating the world’s largest dataset linking experimental data to human clinical outcomes.

03 Mechanistic Reasoning

Reveal a drug’s full risk profile, uncover the mechanisms driving toxicity, and make more better decisions.

Clinical Risk Assessments

Axiom delivers comprehensive clinical risk assessments grounded in AI-powered mechanistic reasoning, helping scientists reduce unexpected toxicity risk earlier in development.

Methods

Axiom’s agents reason over experimental data to predict and explain human drug outcomes

This compound shows strong evidence of bile transport disruption and mitochondrial toxicity, combined with very early ER stress signals in hepatocytes. The phenotypic neighbors from the multicellular system include multiple compounds with Black Box Warnings and market withdrawals for severe hepatotoxicity, including pexidartinib (liver transplant), troglitazone (fatal liver injury, withdrawn), and nefazodone (withdrawn). While direct hepatocyte neighbors show a cleaner profile, the convergence of active experimental signals, high transporter inhibition predictions, and clinical precedent from structurally similar compounds supports a high mechanistic risk assessment.